2011 Fiscal Year Final Research Report
Development of web-based GIS system for visualizing and sharing infectious disease information, and its application to control a risk management for disease outbreaks.
| Project/Area Number |
21590693
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Public health/Health science
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| Research Institution | Tottori University |
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Principal Investigator |
INOUE Masashi 鳥取大学, 総合メディア基盤センター, 准教授 (00176439)
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| Co-Investigator(Kenkyū-buntansha) |
HASEGAWA Shinsaku 北海道立衛生研究所, 研究職員 (70414331)
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| Project Period (FY) |
2009 – 2011
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| Keywords | 医療情報学 |
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| Research Abstract |
A regional characteristics and variations of morbidity for pediatric infectious diseases and influenza are analyzed by use of the specialization coefficient which is presented as ratio between the national average number of reports and regional incidents. Regional morbidities of pediatric infectious diseases and influenza are shown in a bar graph to illustrate transition over 30 years, and specialization coefficient values are displayed in a map graph to clarify the regional characteristics and variations.
A web-based data visualization system was developed to make the infectious disease surveillance data more comprehensible for medical personnel. The main feature of the system is that it automatically generates an animation or graphic images by using the collected numerical data which visualizes the time series and spatial shift of the case incidents of disease simultaneously on the home page. At this time, the information stored in the database system is influenza and eleven pediatric infectious diseases from 1999 to the present related to 47 prefectures in Japan.
In addition, epidemic prediction values for the following year(12 months) were calculated using : a) the EPA(Economic Planning Agency) method and a synthetic method using trend analysis and multiple regression analysis, b) ARIMA FORECAST, and c) analogous vector fitting. The calculated prediction values were compared with actual measured values to discuss the utility of the algorithm.
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